Cambio climático: impactos y perspectivas de investigación desde una visión multidisciplinar

La preocupación que en los últimos años ha surgido por parte de los países desarrollados ante los crecientes impactos económicos generados por el Cambio Climático el cual se refleja en el incremento del número de investigaciones en este campo. La mayoría de los estudios muestran que el Cambio Climático afecta múltiples aspectos del entorno trayendo como resultado impactos directos sobre la economía de un país que en la mayoría de los casos se evidencia en alteraciones positivas y negativas del PIB. Los mayores impactos se presentan en los países del trópico donde paradójicamente hay una cantidad relativamente baja de investigaciones que aborden a profundidad el tema, siendo una posible causa de esto el desconocimiento que se tiene de los conceptos fundamentales relacionados con el tema. Este trabajo muestra desde una revisión actualizada y exhaustiva de la literatura un marco conceptual general que sirve como base para emprender procesos de investigación desde una perspectiva multidisciplinar alrededor de los principales impactos del Cambio Climático

Under Reporting of Dementia Deaths on Death Certificates: A Systematic Review of Population-based Cohort Studies

The purpose of this review is to assess the extent to which dementia is omitted as a cause of death from the death certificates of patients with dementia. A systematic literature search was performed to identify population-based cohort studies in which all participants were examined or screened for symptoms of dementia with a validated instrument followed by confirmation of any suspected cases with a clinical examination (two-phase investigation). Data were extracted in a standardized manner and assessed through the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) initiative. Seven studies met the selection criteria. These were from the Americas (5 articles: 2 from Canada, 2 from the US, and 1 from Brazil) and Europe (2 articles: 1 from the UK and 1 from Spain). Each met at least 83% of the STROBE criteria. The reporting of dementia on death certificates was poor in these 7 studies, ranging from 7.2%-41.8%. Respiratory or circulatory-related problems were the most frequently reported causes of death among people who were demented but who were not reported as demented on death certificates. The use of death certificates for studying dementia grossly underestimates the occurrence of dementia in the population. The poor reporting of dementia on these certificates suggests a lack of awareness of the importance of dementia as a cause of death among medical personnel. There is an urgent need to provide better education on the importance of codification of dementia on death certificates in order to minimize errors in epidemiological studies on dementia.pre-print475 K

Machine learning approaches for detecting parkinson’s disease from EEG analysis: a systematic review.

Background: Diagnosis of Parkinson’s disease (PD) is mainly based on motor symptoms and can be supported by imaging techniques such as the single photon emission computed tomography (SPECT) or M-iodobenzyl-guanidine cardiac scintiscan (MIBG), which are expensive and not always available. In this review, we analyzed studies that used machine learning (ML) techniques to diagnose PD through resting state or motor activation electroencephalography (EEG) tests. Methods: The review process was performed following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. All publications previous to May 2020 were included, and their main characteristics and results were assessed and documented. Results: Nine studies were included. Seven used resting state EEG and two motor activation EEG. Subsymbolic models were used in 83.3% of studies. The accuracy for PD classification was 62–99.62%. There was no standard cleaning protocol for the EEG and a great heterogeneity in the characteristics that were extracted from the EEG. However, spectral characteristics predominated. Conclusions: Both the features introduced into the model and its architecture were essential for a good performance in predicting the classification. On the contrary, the cleaning protocol of the EEG, is highly heterogeneous among the different studies and did not influence the results. The use of ML techniques in EEG for neurodegenerative disorders classification is a recent and growing field.post-print1,30 M

Survey of Machine Learning Techniques in the Analysis of EEG Signals for Parkinson’s Disease: A Systematic Review.

Background: Parkinson’s disease (PD) affects 7–10 million people worldwide. Its diagnosis is clinical and can be supported by image-based tests, which are expensive and not always accessible. Electroencephalograms (EEG) are non-invasive, widely accessible, low-cost tests. However, the signals obtained are difficult to analyze visually, so advanced techniques, such as Machine Learning (ML), need to be used. In this article, we review those studies that consider ML techniques to study the EEG of patients with PD. Methods: The review process was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, which are used to provide quality standards for the objective evaluation of various studies. All publications before February 2022 were included, and their main characteristics and results were evaluated and documented through three key points associated with the development of ML techniques: dataset quality, data preprocessing, and model evaluation. Results: 59 studies were included. The predominating models were Support Vector Machine (SVM) and Artificial Neural Networks (ANNs). In total, 31 articles diagnosed PD with a mean accuracy of 97.35 ± 3.46%. There was no standard cleaning protocol for EEG and a great heterogeneity in EEG characteristics was shown, although spectral features predominated by 88.37%. Conclusions: Neither the cleaning protocol nor the number of EEG channels influenced the classification results. A baseline value was provided for the PD diagnostic problem, although recent studies focus on the identification of cognitive impairment.post-print1392 K

Wearable Technology to Detect Motor Fluctuations in Parkinson’s Disease Patients: Current State and Challenges.

Monitoring of motor symptom fluctuations in Parkinson’s disease (PD) patients is currently performed through the subjective self-assessment of patients. Clinicians require reliable information about a fluctuation’s occurrence to enable a precise treatment rescheduling and dosing adjustment. In this review, we analyzed the utilization of sensors for identifying motor fluctuations in PD patients and the application of machine learning techniques to detect fluctuations. The review process followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Ten studies were included between January 2010 and March 2021, and their main characteristics and results were assessed and documented. Five studies utilized daily activities to collect the data, four used concrete scenarios executing specific activities to gather the data, and only one utilized a combination of both situations. The accuracy for classification was 83.56–96.77%. In the studies evaluated, it was not possible to find a standard cleaning protocol for the signal captured, and there is significant heterogeneity in the models utilized and in the different features introduced in the models (using spatiotemporal characteristics, frequential characteristics, or both). The two most influential factors in the good performance of the classification problem are the type of features utilized and the type of model.post-print900 K

A Survey on 5G Usage Scenarios and Traffic Models

The fifth-generation mobile initiative, 5G, is a tremendous and collective effort to specify, standardize, design, manufacture, and deploy the next cellular network generation. 5G networks will support demanding services such as enhanced Mobile Broadband, Ultra-Reliable and Low Latency Communications and massive Machine-Type Communications, which will require data rates of tens of Gbps, latencies of few milliseconds and connection densities of millions of devices per square kilometer. This survey presents the most significant use cases expected for 5G including their corresponding scenarios and traffic models. First, the paper analyzes the characteristics and requirements for 5G communications, considering aspects such as traffic volume, network deployments, and main performance targets. Secondly, emphasizing the definition of performance evaluation criteria for 5G technologies, the paper reviews related proposals from principal standards development organizations and industry alliances. Finally, well-defined and significant 5G use cases are provided. As a result, these guidelines will help and ease the performance evaluation of current and future 5G innovations, as well as the dimensioning of 5G future deployments.This work is partially funded by the Spanish Ministry of Economy and Competitiveness (project TEC2016-76795-C6-4-R)H2020 research and innovation project 5G-CLARITY (Grant No. 871428)Andalusian Knowledge Agency (project A-TIC-241-UGR18)

Asynchronous Time-Sensitive Networking for Industrial Networks

Time-Sensitive Networking (TSN) is expected to be a cornerstone in tomorrow’s industrial networks. That is because of its ability to provide deterministic quality-of-service in terms of delay, jitter, and scalability. Moreover, it enables more scalable, more affordable, and easier to manage and operate networks compared to current industrial networks, which are based on Industrial Ethernet. In this article, we evaluate the maximum capacity of the asynchronous TSN networks to accommodate industrial traffic flows. To that end, we formally formulate the flow allocation problem in the mentioned networks as a convex mixed-integer non-linear program. To the best of the authors’ knowledge, neither the maximum utilization of the asynchronous TSN networks nor the formulation of the flow allocation problem in those networks have been previously addressed in the literature. The results show that the network topology and the traffic matrix highly impact on the link utilization.This work has been partially funded by the H2020 research and innovation project 5G-CLARITY (Grant No. 871428), national research project TRUE5G: PID2019-108713RB-C5

5G Infrastructure Network Slicing: E2E Mean Delay Model and Effectiveness Assessment to Reduce Downtimes in Industry 4.0

This work has been partially funded by the H2020 project 5G-CLARITY (Grant No. 871428) and the Spanish national project TRUE-5G (PID2019-108713RB-C53).Fifth Generation (5G) is expected to meet stringent performance network requisites of the Industry 4.0. Moreover, its built-in network slicing capabilities allow for the support of the traffic heterogeneity in Industry 4.0 over the same physical network infrastructure. However, 5G network slicing capabilities might not be enough in terms of degree of isolation for many private 5G networks use cases, such as multi-tenancy in Industry 4.0. In this vein, infrastructure network slicing, which refers to the use of dedicated and well isolated resources for each network slice at every network domain, fits the necessities of those use cases. In this article, we evaluate the effectiveness of infrastructure slicing to provide isolation among production lines (PLs) in an industrial private 5G network. To that end, we develop a queuing theory-based model to estimate the end-to-end (E2E) mean packet delay of the infrastructure slices. Then, we use this model to compare the E2E mean delay for two configurations, i.e., dedicated infrastructure slices with segregated resources for each PL against the use of a single shared infrastructure slice to serve the performance-sensitive traffic from PLs. Also we evaluate the use of Time-Sensitive Networking (TSN) against bare Ethernet to provide layer 2 connectivity among the 5G system components. We use a complete and realistic setup based on experimental and simulation data of the scenario considered. Our results support the effectiveness of infrastructure slicing to provide isolation in performance among the different slices. Then, using dedicated slices with segregated resources for each PL might reduce the number of the production downtimes and associated costs as the malfunctioning of a PL will not affect the network performance perceived by the performance-sensitive traffic from other PLs. Last, our results show that, besides the improvement in performance, TSN technology truly provides full isolation in the transport network compared to standard Ethernet thanks to traffic prioritization, traffic regulation, and bandwidth reservation capabilities.H2020 project 5G-CLARITY 871428Spanish Government PID2019-108713RB-C53TRUE-5

A unilateral robotic knee exoskeleton to assess the role of natural gait assistance in hemiparetic patients.

Background: Hemiparetic gait is characterized by strong asymmetries that can severely affect the quality of life of stroke survivors. This type of asymmetry is due to motor deficits in the paretic leg and the resulting compensations in the nonparetic limb. In this study, we aimed to evaluate the effect of actively promoting gait symmetry in hemiparetic patients by assessing the behavior of both paretic and nonparetic lower limbs. This paper introduces the design and validation of the REFLEX prototype, a unilateral active knee–ankle–foot orthosis designed and developed to naturally assist the paretic limbs of hemiparetic patients during gait. Methods: REFLEX uses an adaptive frequency oscillator to estimate the continuous gait phase of the nonparetic limb. Based on this estimation, the device synchronically assists the paretic leg following two different control strategies: (1) replicating the movement of the nonparetic leg or (2) inducing a healthy gait pattern for the paretic leg. Technical validation of the system was implemented on three healthy subjects, while the effect of the generated assistance was assessed in three stroke patients. The effects of this assistance were evaluated in terms of interlimb symmetry with respect to spatiotemporal gait parameters such as step length or time, as well as the similarity between the joint’s motion in both legs. Results: Preliminary results proved the feasibility of the REFLEX prototype to assist gait by reinforcing symmetry. They also pointed out that the assistance of the paretic leg resulted in a decrease in the compensatory strategies developed by the nonparetic limb to achieve a functional gait. Notably, better results were attained when the assistance was provided according to a standard healthy pattern, which initially might suppose a lower symmetry but enabled a healthier evolution of the motion of the nonparetic limb. Conclusions: This work presents the preliminary validation of the REFLEX prototype, a unilateral knee exoskeleton for gait assistance in hemiparetic patients. The experimental results indicate that assisting the paretic leg of a hemiparetic patient based on the movement of their nonparetic leg is a valuable strategy for reducing the compensatory mechanisms developed by the nonparetic limb.post-print6406 K

Folding of cytosine-based nucleolipid monolayer by guanine recognition at the air-water interface

Monolayers of a cytosine-based nucleolipid (1,2-dipalmitoyl-sn-glycero-3-(cytidine diphosphate) (ammonium salt), CDP-DG) at basic subphase have been prepared at the air-water interface both in absence and presence of guanine. The formation of the complementary base pairing is demonstrated by combining surface experimental techniques, i.e., surface pressure (π)–area (A), Brewster angle microscopy (BAM), infrared spectroscopy (PM-IRRAS) and computer simulations. A folding of the cytosine-based nucleolipid molecules forming monolayer at the air-water interface occurs during the guanine recognition as absorbate host and is kept during several compression-expansion processes under set experimental conditions. The specificity between nitrogenous bases has been also registered. Finally, mixed monolayers of CDP-DG and a phospholipid (1,2-dimyristoyl-sn-glycero-3-phosphate (sodium salt), DMPA) has been studied and a molecular segregation of the DMPA molecules has been inferred by the additivity rule